Multiscale texture features for the retrieval of high resolution satellite images

With the steadily expanding demand for remote sensing images, many satellites have been launched, and thousands of high resolution satellite images (HRSI) are acquired every day. Therefore, retrieving useful images quickly and accurately from a huge image database has become a challenge. In this paper, we propose an adaptive content-based image retrieval (CBIR) system for the retrieval of HRSI on the basis of Steerable Pyramids using RGB and CIElab color systems. The texture feature vectors are extracted by calculating the statistical measures of decomposed image sub-bands. To improve the performances of our CBIR scheme, the system rotation and scale invariance is enhanced by introducing a circular shifting of the feature vector elements according to each scale. Extensive experiments were conducted firstly using 8 image classes from land-use/land-cover (LULC) UCMerced dataset. Obtained results are compared with color Gabor opponent texture features. The system was then extended to work on the whole dataset consisting of 21 image classes, and compared with results obtained from SIFT descriptor. The tests and evaluation measures demonstrate that the proposed system gives a good performance in terms of high precision.